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Yaktine, and Heather B. R Any other unique identifying number, characteristic, or code, except as permitted by paragraph c of this section; and. Covered entities may include the first three digits of the ZIP code if, according to the current publicly available data from the Bureau of the Census: 1 The geographic unit formed by combining all ZIP codes with the same three initial digits contains more than 20, people; or 2 the initial three digits of a ZIP code for all such geographic units containing 20, or fewer people is changed to This means that the initial three digits of ZIP codes may be included in de-identified information except when the ZIP codes contain the initial three digits listed in the Table below.

In those cases, the first three digits must be listed as Utilizing Census data, the following three-digit ZCTAs have a population of 20, or fewer persons. To produce a de-identified data set utilizing the safe harbor method, all records with three-digit ZIP codes corresponding to these three-digit ZCTAs must have the ZIP code changed to Covered entities should not, however, rely upon this listing or the one found in the August 14, regulation if more current data has been published.

This new methodology also is briefly described below, as it will likely be of interest to all users of data tabulated by ZIP code. The Census Bureau will not be producing data files containing U. Zip codes can cross State, place, county, census tract, block group, and census block boundaries.

The geographic designations the Census Bureau uses to tabulate data are relatively stable over time. For instance, census tracts are only defined every ten years. In contrast, ZIP codes can change more frequently.

Postal Service ZIP codes. ZCTAs are generalized area representations of U. The Bureau of the Census provides information regarding population density in the United States. Covered entities are expected to rely on the most current publicly available Bureau of Census data regarding ZIP codes. The information is derived from the Decennial Census and was last updated in It is expected that the Census Bureau will make data available from the Decennial Census in the near future.

This guidance will be updated when the Census makes new information available. For example, a data set that contained patient initials, or the last four digits of a Social Security number, would not meet the requirement of the Safe Harbor method for de-identification.

Elements of dates that are not permitted for disclosure include the day, month, and any other information that is more specific than the year of an event. Many records contain dates of service or other events that imply age. Ages that are explicitly stated, or implied, as over 89 years old must be recoded as 90 or above. Dates associated with test measures, such as those derived from a laboratory report, are directly related to a specific individual and relate to the provision of health care.

Such dates are protected health information. As a result, no element of a date except as described in 3. This category corresponds to any unique features that are not explicitly enumerated in the Safe Harbor list A-Q , but could be used to identify a particular individual. Thus, a covered entity must ensure that a data set stripped of the explicitly enumerated identifiers also does not contain any of these unique features.

The following are examples of such features:. Identifying Number There are many potential identifying numbers. Identifying Code A code corresponds to a value that is derived from a non-secure encoding mechanism. For instance, a code derived from a secure hash function without a secret key e. This is because the resulting value would be susceptible to compromise by the recipient of such data.

As another example, an increasing quantity of electronic medical record and electronic prescribing systems assign and embed barcodes into patient records and their medications. See the discussion of re-identification. Identifying Characteristic A characteristic may be anything that distinguishes an individual and allows for identification.

Generally, a code or other means of record identification that is derived from PHI would have to be removed from data de-identified following the safe harbor method. The objective of the paragraph is to permit covered entities to assign certain types of codes or other record identification to the de-identified information so that it may be re-identified by the covered entity at some later date.

In the context of the Safe Harbor method, actual knowledge means clear and direct knowledge that the remaining information could be used, either alone or in combination with other information, to identify an individual who is a subject of the information. This means that a covered entity has actual knowledge if it concludes that the remaining information could be used to identify the individual. The covered entity, in other words, is aware that the information is not actually de-identified information.

Example 2: Clear Familial Relation Imagine a covered entity was aware that the anticipated recipient, a researcher who is an employee of the covered entity, had a family member in the data e. In addition, the covered entity was aware that the data would provide sufficient context for the employee to recognize the relative. In this situation, the risk of identification is of a nature and degree that the covered entity must have concluded that the recipient could clearly and directly identify the individual in the data.

Example 3: Publicized Clinical Event Rare clinical events may facilitate identification in a clear and direct manner. For instance, imagine the information in a patient record revealed that a patient gave birth to an unusually large number of children at the same time.

During the year of this event, it is highly possible that this occurred for only one individual in the hospital and perhaps the country. As a result, the event was reported in the popular media, and the covered entity was aware of this media exposure. In this case, the risk of identification is of a nature and degree that the covered entity must have concluded that the individual subject of the information could be identified by a recipient of the data.

In this situation, the covered entity has actual knowledge because it was informed outright that the recipient can identify a patient, unless it subsequently received information confirming that the recipient does not in fact have a means to identify a patient. Much has been written about the capabilities of researchers with certain analytic and quantitative capacities to combine information in particular ways to identify health information.

OCR does not expect a covered entity to presume such capacities of all potential recipients of de-identified data. This would not be consistent with the intent of the Safe Harbor method, which was to provide covered entities with a simple method to determine if the information is adequately de-identified. Only names of the individuals associated with the corresponding health information i. There is no explicit requirement to remove the names of providers or workforce members of the covered entity or business associate.

At the same time, there is also no requirement to retain such information in a de-identified data set. Beyond the removal of names related to the patient, the covered entity would need to consider whether additional personal names contained in the data should be suppressed to meet the actual knowledge specification. Additionally, other laws or confidentiality concerns may support the suppression of this information.

However, nothing prevents a covered entity from asking a recipient of de-identified information to enter into a data use agreement, such as is required for release of a limited data set under the Privacy Rule.

This agreement may prohibit re-identification. Of course, the use of a data use agreement does not substitute for any of the specific requirements of the Safe Harbor method. PHI may exist in different types of data in a multitude of forms and formats in a covered entity. This data may reside in highly structured database tables, such as billing records.

Yet, it may also be stored in a wide range of documents with less structure and written in natural language, such as discharge summaries, progress notes, and laboratory test interpretations. These documents may vary with respect to the consistency and the format employed by the covered entity.

The de-identification standard makes no distinction between data entered into standardized fields and information entered as free text i. Whether additional information must be removed falls under the actual knowledge provision; the extent to which the covered entity has actual knowledge that residual information could be used to individually identify a patient.

In structured documents, it is relatively clear which fields contain the identifiers that must be removed following the Safe Harbor method. For instance, it is simple to discern when a feature is a name or a Social Security Number, provided that the fields are appropriately labeled. However, many researchers have observed that identifiers in medical information are not always clearly labeled. It also is important to document when fields are derived from the Safe Harbor listed identifiers.

For instance, if a field corresponds to the first initials of names, then this derivation should be noted. De-identification is more efficient and effective when data managers explicitly document when a feature or value pertains to identifiers. Health Level 7 HL7 and the International Standards Organization ISO publish best practices in documentation and standards that covered entities may consult in this process. The covered entity must remove this information. The phrase may be retained in the data.

Note: some of these terms are paraphrased from the regulatory text; please see the HIPAA Rules for actual definitions. Information that is a subset of health information, including demographic information collected from an individual, and: 1 Is created or received by a health care provider, health plan, employer, or health care clearinghouse; and 2 Relates to the past, present, or future physical or mental health or condition of an individual; the provision of health care to an individual; or the past, present, or future payment for the provision of health care to the individual; and i That identifies the individual; or ii With respect to which there is a reasonable basis to believe the information can be used to identify the individual.

In an effort to make this guidance a useful tool for HIPAA covered entities and business associates, we welcome and appreciate your sending us any feedback or suggestions to improve this guidance. You may submit a comment by sending an e-mail to ocrprivacy hhs. OCR gratefully acknowledges the significant contributions made by Bradley Malin, PhD, to the development of this guidance, through both organizing the workshop and synthesizing the concepts and perspectives in the document itself.

OCR also thanks the workshop panelists for generously providing their expertise and recommendations to the Department. To sign up for updates or to access your subscriber preferences, please enter your contact information below. Washington, D. A-Z Index. General 1.

Guidance on Satisfying the Safe Harbor Method 3. Protected health information includes many common identifiers e. Back to top De-identification and its Rationale The increasing adoption of health information technologies in the United States accelerates their potential to facilitate beneficial studies that combine large, complex data sets from multiple sources.

Back to top The De-identification Standard Section Re-identification The implementation specifications further provide direction with respect to re-identification , specifically the assignment of a unique code to the set of de-identified health information to permit re-identification by the covered entity. Back to top Preparation for De-identification The importance of documentation for which values in health data correspond to PHI, as well as the systems that manage PHI, for the de-identification process cannot be overstated.

Back to top What is an acceptable level of identification risk for an expert determination? Back to top How long is an expert determination valid for a given data set? Back to top Can an expert derive multiple solutions from the same data set for a recipient? Back to top How do experts assess the risk of identification of information? Principles used by experts in the determination of the identifiability of health information. Principle Description Examples Replicability Prioritize health information features into levels of risk according to the chance it will consistently occur in relation to the individual.

Low: The results of laboratory reports are not often disclosed with identity beyond healthcare environments. High: Patient name and demographics are often in public data sources, such as vital records -- birth, death, and marriage registries. This means that very few residents could be identified through this combination of data alone.

This means that over half of U. Assess Risk The greater the replicability, availability, and distinguishability of the health information, the greater the risk for identification. Low: Laboratory values may be very distinguishing, but they are rarely independently replicable and are rarely disclosed in multiple data sources to which many people have access.

High: Demographics are highly distinguishing, highly replicable, and are available in public data sources. Table 2. An example of protected health information. Table 3. A version of Table 2 with suppressed patient values. Table 4. A version of Table 2 with generalized patient values. Table 5. A version of Table 2 with randomized patient values. Table 6. A version of Table 2 that is 2-anonymized.

Business Associate A person or entity that performs certain functions or activities that involve the use or disclosure of protected health information on behalf of, or provides services to, a covered entity. A covered health care provider, health plan, or health care clearinghouse can be a business associate of another covered entity.

Covered Entity Any entity that is a health care provider that conducts certain transactions in electronic form called here a "covered health care provider".

Cryptographic Hash Function A hash function that is designed to achieve certain security properties. The sharing of PHI outside of the health care component of a covered entity is a disclosure. Hash Function A mathematical function which takes binary data, called the message, and produces a condensed representation, called the message digest.

Individually Identifiable Health Information Information that is a subset of health information, including demographic information collected from an individual, and: 1 Is created or received by a health care provider, health plan, employer, or health care clearinghouse; and 2 Relates to the past, present, or future physical or mental health or condition of an individual; the provision of health care to an individual; or the past, present, or future payment for the provision of health care to the individual; and i That identifies the individual; or ii With respect to which there is a reasonable basis to believe the information can be used to identify the individual.

Protected Health Information Individually identifiable health information: 1 Except as provided in paragraph 2 of this definition, that is: i Transmitted by electronic media; ii Maintained in electronic media; or iii Transmitted or maintained in any other form or medium.

Suppression Withholding information in selected records from release. Protected health information PHI is defined as individually identifiable health information transmitted or maintained by a covered entity or its business associates in any form or medium 45 CFR The definition exempts a small number of categories of individually identifiable health information, such as individually identifiable health information found in employment records held by a covered entity in its role as an employer.

Report on statistical disclosure limitation methodology. May Revised by the Confidentiality and Data Access Committee. This table was adapted from B. Malin, D. Karp, and R. Technical and policy approaches to balancing patient privacy and data sharing in clinical and translational research.

Journal of Investigative Medicine. Although risk actually is more of a continuum, this rough partition illustrates how context impacts risk. See L. August 23, See P.

Revisiting the uniqueness of simple demographics in the US population. K-anonymity: a model for protecting privacy. See K. Benitez and B. Journal of the American Medical Informatics Association. See M. Elliot, C. Skinner, and A. Special unique, random unique and sticky populations: some counterintuitive effects of geographic detail on disclosure risk. Research in Official Statistics.

See G. Duncan, S. Keller-McNulty, and S. Lynne Stokes. Disclosure risk vs. El Emam and F. Protecting privacy using k-anonymity. First published: May Guidelines for working with small numbers. Policy for disclosure of reportable disease information.

Klein, S. Proctor, M. Boudreault, and K. Healthy people criteria for data suppression. National Center for Health Statistics. Staff Manual on Confidentiality. Section 9: Avoiding inadvertent disclosures through release of microdata; Section Avoiding inadvertent disclosures in tabular data. Socioeconomic Data and Applications Center. Confidentiality issues and policies related to the utilization and dissemination of geospatial data for public health application; a report to the public health applications of earth science program, national aeronautics and space administration, science mission directorate, applied sciences program.

Utah State Department of Health. First published: Washington State Department of Health. First published , last updated October El Emam, et al. A globally optimal k -anonymity method for the de-Identification of health information. McCallister, T. Grance, and K. Guide to protecting the confidentiality of personally identifiable information pii : recommendations of the National Institute of Standards and Technology.

For more information about data use agreements please see the following: Subcommittee on Disclosure Limitation Methodology, Federal Committee on Statistical Methodology. El Emam, F. Dankar, R. Vaillancourt, T. Roffey, and M. Evaluating the risk of re-identification of patients from hospital prescription records.

Canadian Journal of Hospital Pharmacy. Loukides, J. Denny, and B. The disclosure of diagnosis codes can breach research participants privacy. Malin and L. How not to protect genomic data privacy in a distributed network: using trail re-identification to evaluate and design anonymity protection systems.

Journal of Biomedical Informatics. Washington, DC. March , Dorr, W. Phillips, S. Phansalkar, S. Sims, and J. Assessing the difficulty and time cost of de-identification in clinical narratives.